Fault diagnosis of axle-box bearing in electric multiple unit based on transfer learning network with feedback control input strategy

Abstract Aiming at several problems in the practical application of axle-box bearing fault diagnosis for electric multiple unit, a fault diagnosis method based on a transfer learning (TL) network with a feedback control input strategy is proposed. First, given the unavailability of large amounts of labeled data, the proposed method employs a TL network as its central framework, incorporating a lightweight architecture. Maximum mean discrepancy and classification loss are also introduced to align the extracted features, allowing the method to perform fault diagnosis across various bearings under different operating conditions. Then, considering the various perturbations in the vibration data, an adaptive input strategy combining signal processing methods is proposed to screen out important information through denoising and deharmonizing, thereby enhancing input data quality. Furthermore, a feedback control input strategy is proposed to reduce redundant health information input, inspired by feedback control principles. Finally, a fault identification strategy is implemented to recognize fault types in unknown signals. The accuracy and practicality of the proposed method are validated through experiments on three datasets and random fault vibration signals.